System and Method for Classifying a Tooth Condition Based on Landmarked Anthropomorphic Measurements.

ABSTRACT

Disclosed is a system and method for classifying a tooth condition, comprising: a localization layer; a classification layer; a processor; a non-transitory storage element coupled to the processor; encoded instructions stored in the non-transitory storage element, wherein the encoded instructions when implemented by the processor, configure the automated parsing pipeline system to: localize a present anatomical landmark in a patient&#39;s oral or maxillofacial region depicted in a parsed image frame by the localization layer, said landmark comprising of a feature or features with anatomical or pathological significance; and classify a dental or maxillofacial condition based on measuring any kind of relation between two or more features within and/or between landmarks by the classification layer.

BACKGROUND Field

This invention relates generally to medical diagnostics, and morespecifically to an automated parsing pipeline system and method foranatomical localization and condition classification. More specifically,it relates to the construction of an EoI-focused panorama of a teetharch. Further, it relates to a system and method for classifying a toothcondition based on landmarked anthropomorphic measurements.

Related Art

Modern image generation systems play an important role in diseasedetection and treatment planning. Few existing systems and methods werediscussed as follows. One common method utilized is dental radiography,which provides dental radiographic images that enable the dentalprofessional to identify many conditions that may otherwise goundetected and to see conditions that cannot be identified clinically.Another technology is cone beam computed tomography (CBCT) that allowsto view structures in the oral-maxillofacial complex in threedimensions. Hence, cone beam computed tomography technology is mostdesired over the dental radiography.

However, CBCT includes one or more limitations, such as time consumptionand complexity for personnel to become fully acquainted with the imagingsoftware and correctly using digital imaging and communications inmedicine (DICOM) data. American Dental Association (ADA) also suggeststhat the CBCT image should be evaluated by a dentist with appropriatetraining and education in CBCT interpretation. Further, many dentalprofessionals who incorporate this technology into their practices havenot had the training required to interpret data on anatomic areas beyondthe maxilla and the mandible. To address the foregoing issues, deeplearning has been applied to various medical imaging problems tointerpret the generated images, but its use remains limited within thefield of dental radiography. Further, most applications only work with2D X-ray images.

Another existing article entitled “Teeth and jaw 3D reconstruction instomatology”, Proceedings of the International Conference on MedicalInformation Visualisation—BioMedical Visualisation, pp 23-28, 2007,researchers Krsek et al. describe a method dealing with problems of 3Dtissue reconstruction in stomatology. In this process, 3D geometrymodels of teeth and jaw bones were created based on input (computedtomography) CT image data. The input discrete CT data were segmented bya nearly automatic procedure, with manual correction and verification.Creation of segmented tissue 3D geometry models was based onvectorization of input discrete data extended by smoothing anddecimation. The actual segmentation operation was primarily based onselecting a threshold of Hounsfield Unit values. However, this methodfails to be sufficiently robust for practical use.

Another existing patent number U.S. Pat. No. 8,849,016, entitled“Panoramic image generation from CBCT dental images” to Shoupu Chen etal. discloses a method for forming a panoramic image from a computedtomography image volume, acquires image data elements for one or morecomputed tomographic volume images of a subject, identifies a subset ofthe acquired computed tomographic images that contain one or morefeatures of interest and defines, from the subset of the acquiredcomputed tomographic images, a sub-volume having a curved shape thatincludes one or more of the contained features of interest. The curvedshape is unfolded by defining a set of unfold lines wherein each unfoldline extends at least between two curved surfaces of the curved shapesub-volume and re-aligning the image data elements within the curvedshape sub-volume according to a re-alignment of the unfold lines. One ormore views of the unfolded sub-volume are displayed.

Another existing patent application number US20080232539, entitled“Method for the reconstruction of a panoramic image of an object, and acomputed tomography scanner implementing said method” to AlessandroPasini et al. discloses a method for the reconstruction of a panoramicimage of the dental arches of a patient, a computer program product, anda computed tomography scanner implementing said method. The methodinvolves acquiring volumetric tomographic data of the object; extractingfrom the volumetric tomographic data tomographic data corresponding toat least three sections of the object identified by respective mutuallyparallel planes; determining on each section extracted, a respectivetrajectory that a profile of the object follows in an area correspondingto said section; determining a first surface transverse to said planessuch as to comprise the trajectories, and generating the panoramic imageon the basis of a part of the volumetric tomographic data identified asa function of said surface. However, the above references also fail toaddress the afore discussed problems regarding the cone beam computedtomography technology and image generation system, not to mention anautomated anatomical localization and pathology detection/classificationmeans.

Furthermore, there is a need for classifying a tooth condition based onlocalizing a tooth landmark and based on features of said landmark,determine a tooth condition. Therefore, there is a need for an automatedparsing pipeline system and method for anatomical and/or landmarklocalization for condition classification, with minimal image analysistraining and visual ambiguities.

SUMMARY

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions.Embodiments disclosed include an automated parsing pipeline system andmethod for anatomical localization and condition classification. Furtherembodiments disclosed include a method and system for constructing apanorama with elements of interest (EoI) emphasized of a teeth arch orany point of interest in a oral-maxillofacial complex.

In an embodiment, the system comprises an input event source, a memoryunit in communication with the input event source, a processor incommunication with the memory unit, a volumetric image processor incommunication with the processor, a voxel parsing engine incommunication with the volumetric image processor and a localizing layerin communication with the voxel parsing engine. In one embodiment, thememory unit is a non-transitory storage element storing encodedinformation. In one embodiment, at least one volumetric image data isreceived from the input event source by the volumetric image processor.In one embodiment, the input event source is a radio-image gatheringsource.

The processor is configured to parse the at least one receivedvolumetric image data into at least a single image frame field of viewby the volumetric image processor. The processor is further configuredto localize anatomical structures residing in the at least single fieldof view by assigning at least one of a pixel and voxel a distinctanatomical structure by the voxel parsing engine. In one embodiment, thesingle image frame field of view is pre-processed for localization,which involves rescaling using linear interpolation. The pre-processinginvolves use of any one of a normalization schemes to account forvariations in image value intensity depending on at least one of aninput or output of volumetric image. In one embodiment, localization isachieved using any one of a fully convolutional network (FCN) or plainclassification convolutional neural network (CNN).

The processor is further configured to select at least one of all pixelsand voxels (p/v) belonging to the localized anatomical structure byfinding a minimal bounding rectangle around the p/v and the surroundingregion for cropping as a defined anatomical structure by thelocalization layer. The bounding rectangle extends equally in alldirections to capture the tooth and surrounding context. In oneembodiment, the automated parsing pipeline system further comprises adetection module. The processor is configured to detect or classify theconditions for each defined anatomical structure within the croppedimage by a detection module or classification layer. In one embodiment,the classification is achieved using any one of a fully convolutionalnetwork or plain classification convolutional neural network (FCN/CNN).

In another embodiment, an automated parsing pipeline method foranatomical localization and condition classification is disclosed. Atone step, at least one volumetric image data is received from an inputevent source by a volumetric image processor. At another step, thereceived volumetric image data is parsed into at least a single imageframe field of view by the volumetric image processor. At another step,the single image frame field of view is pre-processed by controllingimage intensity value by the volumetric image processor. At anotherstep, the anatomical structure residing in the single pre-processedfield of view is localized by assigning each p/v a distinct anatomicalstructure ID by the voxel parsing engine. At another step, all p/vbelonging to the localized anatomical structure is selected by finding aminimal bounding rectangle around the voxels and the surrounding regionfor cropping as a defined anatomical structure by the localizationlayer. In another embodiment, the method includes a step of, classifyingthe conditions for each defined anatomical structure within the croppedimage by the classification layer.

In another embodiment, an automated parsing pipeline method for landmarklocalization and condition classification is disclosed. At one step, atleast one volumetric image data is received from an input event sourceby a volumetric image processor. At another step, the receivedvolumetric image data is parsed into at least a single image frame fieldof view by the volumetric image processor. At another step, a landmarkstructure residing in the single field of view is localized by assigningeach p/v a distinct landmark structure ID by the voxel parsing engine.At another step, all p/v belonging to the localized landmark structureis selected by finding a minimal bounding rectangle around the voxelsand the surrounding region for cropping as a defined landmark structureby the localization layer. In another embodiment, the method furtherincludes a step of, classifying the conditions for each defined landmarkstructure within the cropped image by the classification layer byperforming anthropomorphic measurements around certain features of alandmark or landmarks.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates in a block diagram, an automated parsing pipelinesystem for anatomical localization and condition classification,according to an embodiment.

FIG. 1B illustrates in a block diagram, an automated parsing pipelinesystem for anatomical localization and condition classification,according to another embodiment.

FIG. 2A illustrates in a block diagram, an automated parsing pipelinesystem for anatomical localization and condition classificationaccording to yet another embodiment.

FIG. 2B illustrates in a block diagram, a processor system according toan embodiment.

FIG. 3A illustrates in a flow diagram, an automated parsing pipelinemethod for anatomical localization and condition classification,according to an embodiment.

FIG. 3B illustrates in a flow diagram, an automated parsing pipelinemethod for anatomical localization and condition classification,according to another embodiment.

FIG. 4 illustrates in a block diagram, the automated parsing pipelinearchitecture according to an embodiment.

FIG. 5 illustrates in a screenshot, an example of ground truth andpredicted masks in an embodiment of the present invention.

FIG. 6A illustrates in a screenshot, the extraction of anatomicalstructure by the localization model of the system in an embodiment ofthe present invention.

FIG. 6B illustrates in a screenshot, the extraction of anatomicalstructure by the localization model of the system in an embodiment ofthe present invention.

FIG. 6C illustrates in a screenshot, the extraction of anatomicalstructure by the localization model of the system in an embodiment ofthe present invention.

FIG. 7 illustrates in a graph, receiver operating characteristic (ROC)curve of a predicted tooth condition in an embodiment of the presentinvention.

FIG. 8 illustrates a method flow diagram of exemplary steps in theconstruction of an EoI-focused panorama in accordance with an aspect ofthe invention.

FIG. 9 illustrates in a screen-shot, an exemplary constructed panoramain accordance with an aspect of the invention.

FIG. 10 depicts a system block diagram of the EoI-focused panoramaconstruction in accordance with an aspect of the invention.

FIG. 11 depicts a system block diagram of the automated parsing pipelinefor landmark localization and condition classification in accordancewith an aspect of the invention.

FIG. 12 depicts an interaction flow diagram of the automated parsingpipeline for landmark localization and condition classification inaccordance with an aspect of the invention.

FIG. 13 depicts a method flow diagram of the automated parsing pipelinefor landmark localization and condition classification in accordancewith an aspect of the invention.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying FIGS. 1A-7. In the following detaileddescription of embodiments of the invention, numerous specific detailsare set forth in order to provide a more thorough understanding of theinvention. In other instances, well-known features have not beendescribed in detail to avoid obscuring the invention. Embodimentsdisclosed include an automated parsing pipeline system and method foranatomical localization and condition classification.

FIG. 1A illustrates a block diagram 100 of the system comprising aninput event source 101, a memory unit 102 in communication with theinput event source 101, a processor 103 in communication with the memoryunit 102, a volumetric image processor 103 a in communication with theprocessor 103, a voxel parsing engine 104 in communication with thevolumetric image processor 103 a and a localizing layer 105 incommunication with the voxel parsing engine 104. In an embodiment, thememory unit 102 is a non-transitory storage element storing encodedinformation. The encoded instructions when implemented by the processor103, configure the automated pipeline system to localize an anatomicalstructure and classify the condition of the localized anatomicalstructure.

In one embodiment, an input data is provided via the input event source101. In one embodiment, the input data is a volumetric image data andthe input event source 101 is a radio-image gathering source. In oneembodiment, the input data is a 2-Dimensional (2D) image data. Inanother embodiment, the input data is a 3-Dimensional (3D) image data.The volumetric image processor 103 a is configured to receive thevolumetric image data from the radio-image gathering source. Initially,the volumetric image data is pre-processed, which involves conversion of3-D pixel array into an array of Hounsfield Unit (HU) radio intensitymeasurements.

The processor 103 is further configured to parse at least one receivedimage or volumetric image data 103 b (i/v.i) into at least a singleimage frame field of view by the volumetric image processor. Theprocessor 103 is further configured to localize anatomical structuresresiding in the single image frame field of view by assigning at leastone of each a pixel or voxel (p/v) a distinct anatomical structure bythe voxel parsing engine 104. In one embodiment, the single image framefield of view is pre-processed for localization, which involvesrescaling using linear interpolation. The pre-processing involves use ofany one of a normalization schemes to account for variations in imagevalue intensity depending on at least one of an input or output of animage or volumetric image (i/v.i). In one embodiment, localization isachieved using any one of fully convolutional network or plainclassification convolutional neural network (FCN/CNN), such as aV-Net-based fully convolutional neural network. In one embodiment, theV-Net is a 3D generalization of UNet.

The processor 103 is further configured to select all p/v belonging tothe localized anatomical structure by finding a minimal boundingrectangle around the p/v and the surrounding region for cropping as adefined anatomical structure by the localization layer. The boundingrectangle extends equally in all directions to capture the tooth andsurrounding context. In one embodiment, the bounding rectangle mayextend 8-15 mm in all directions to capture the tooth and surroundingcontext.

FIG. 1B illustrates in a block diagram 110, an automated parsingpipeline system for anatomical localization and conditionclassification, according to another embodiment. The automated parsingpipeline system further comprises a detection module 106. The processor103 is configured to detect or classify the conditions for each definedanatomical structure within the cropped image by a detection module orclassification layer 106. In one embodiment, the classification isachieved using any one of a FCN/CNN, such as a DenseNet 3-Dconvolutional neural network.

In one embodiment, the localization layer 105 includes 33 class semanticsegmentation in 3D. In one embodiment, the system is configured toclassify each p/v as one of 32 teeth or background and resultingsegmentation assigns each p/v to one of 33 classes. In anotherembodiment, the system is configured to classify each p/v as eithertooth or other anatomical structure of interest. In case of localizingonly teeth, the classification includes, but not limited to, 2 classes.Then individual instances of every class (teeth) could be split, e.g. byseparately predicting a boundary between them. In some embodiments, theanatomical structure being localized, includes, but not limited to,teeth, upper and lower jaw bone, sinuses, lower jaw canal and joint.

In one embodiment, the system utilizes fully-convolutional network. Inanother embodiment, the system works on downscaled images (typicallyfrom 0.1-0.2 mm i/v.i resolution to 1.0 mm resolution) and grayscale(1-channel) image (say, 1×100×100×100-dimensional tensor). In yetanother embodiment, the system outputs 33-channel image (say,33×100×100×100-dimensional tensor) that is interpreted as a probabilitydistribution for non-tooth vs. each of 32 possible (for adult human)teeth, for every p/v.

In an alternative embodiment, the system provides 2-class segmentation,which includes labelling or classification, if the localizationcomprises tooth or not. The system additionally outputs assignment ofeach tooth p/v to a separate “tooth instance”.

In one embodiment, the system comprises FCN/CNN (such as VNet)predicting multiple “energy levels”, which are later used to findboundaries. In another embodiment, a recurrent neural network could beused for step by step prediction of tooth, and keep track of the teeththat were outputted a step before. In yet another embodiment, Mask-RCNNgeneralized to 3D could be used by the system. In yet anotherembodiment, the system could take multiple crops from 3D image inoriginal resolution, perform instance segmentation, and then join cropsto form mask for all original image. In another embodiment, the systemcould apply either segmentation or object detection in 2D, to segmentaxial slices. This would allow to process images in original resolution(albeit in 2D instead of 3D) and then infer 3D shape from 2Dsegmentation.

In one embodiment, the system could be implemented utilizing descriptorlearning in the multitask learning framework i.e., a single networklearning to output predictions for multiple dental conditions. Thiscould be achieved by balancing loss between tasks to make sure everyclass of every task have approximately same impact on the learning. Theloss is balanced by maintaining a running average gradient that networkreceives from every class*task and normalizing it. Alternatively,descriptor learning could be achieved by teaching network on batchesconsisting data about a single condition (task) and sample examples intothese batches in such a way that all classes will have same number ofexamples in batch (which is generally not possible in multitask setup).Further, standard data augmentation could be applied to 3D tooth imagesto perform scale, crop, rotation, vertical flips. Then, combining allaugmentations and final image resize to target dimensions in a singleaffine transform and apply all at once.

Advantageously, in some embodiments, to accumulate positive casesfaster, a weak model could be trained and ran for all of the unlabeleddata. From resulting predictions, teeth model that yield high scores onsome rare pathology of interest are selected. Then the teeth are sent tobe labelled and added to the dataset (both positive and negativelabels). This allows to quickly and cost-efficiently build up a morebalanced dataset for rare pathologies.

In some embodiments, the system could use coarse segmentation mask fromlocalizer as an input instead of tooth image. In some embodiments, thedescriptor could be trained to output fine segmentation mask from someof the intermediate layers. In some embodiments, the descriptor could betrained to predict tooth number.

As an alternative to multitask learning approach, “one network percondition” could be employed, i.e. models for different conditions arecompletely separate models that share no parameters. Another alternativeis to have a small shared base network and use separate subnetworksconnected to this base network, responsible for specificconditions/diagnoses.

FIG. 2A illustrates in a block diagram 200, an automated parsingpipeline system for anatomical localization and condition classificationaccording to yet another embodiment. In an embodiment, the systemcomprises an input system 204, an output system 202, a memory system orunit 206, a processor system 208, an input/output system 214 and aninterface 212. Referring to FIG. 2B, the processor system 208 comprisesa volumetric image processor 208 a, a voxel parsing engine 208 b incommunication with the volumetric image processor 208 a, a localizationlayer 208 c in communication with the voxel parsing engine 208 and adetection module 208 d in communication with the localization module 208c. The processor 208 is configured to receive at least one i/v.i via aninput system 202. At least one received i/v.i, which may be comprised ofa 2-D or 3-D image. The pixel array is pre-processed to convert into anarray of Hounsfield Unit (HU) radio intensity measurements. Then, theprocessor 208 is configured to parse the received i/v.i into at least asingle image frame field of view by the said volumetric image processor208 a.

The anatomical structures residing in the at least single field of viewis localized by assigning each p/v a distinct anatomical structure bythe voxel parsing engine 208 b. The processor 208 is configured toselect all p/v belonging to the localized anatomical structure byfinding a minimal bounding rectangle around the p/v and the surroundingregion for cropping as a defined anatomical structure by thelocalization layer 208 c. Then, the conditions for each definedanatomical structure within the cropped image is classified by adetection module or classification layer 208 d.

FIG. 3A illustrates in a flow diagram 300, an automated parsing pipelinemethod for anatomical localization and condition classification,according to an embodiment. At step 301, an input image data isreceived. In one embodiment, the image data is a i/v.i. At step 302, thereceived i/v.i is parsed into at least a single image frame field ofview. The parsed i/v.i is pre-processed by controlling image intensityvalue.

At step 304, a tooth or anatomical structure inside the pre-processedand parsed i/v.i is localized and identified by tooth number. At step306, the identified tooth and surrounding context within the localizedi/v.i are extracted. At step 308, a visual report is reconstructed withlocalized and defined anatomical structure. In some embodiments, thevisual reports include, but not limited to, an endodontic report (withfocus on tooth's root/canal system and its treatment state), animplantation report (with focus on the area where the tooth is missing),and a dystopic tooth report for tooth extraction (with focus on the areaof dystopic/impacted teeth).

FIG. 3B illustrates in a flow diagram 310, an automated parsing pipelinemethod for anatomical localization and condition classification,according to another embodiment. At step 312, at least one i/v.i isreceived from a radio-image gathering source by a volumetric imageprocessor.

At step 314, the received i/v.i is parsed into at least a single imageframe field of view by the volumetric image processor. At least singleimage frame field of view is pre-processed by controlling imageintensity value by the volumetric image processor. At step 316, ananatomical structure residing in the at least single pre-processed fieldof view is localized by assigning each p/v a distinct anatomicalstructure ID by the voxel parsing engine. At step 318, all p/v belongingto the localized anatomical structure is selected by finding a minimalbounding rectangle around the p/v and the surrounding region forcropping as a defined anatomical structure by the localization layer. Atstep 320, a visual report is reconstructed with defined and localizedanatomical structure. At step 322, conditions for each definedanatomical structure is classified within the cropped image by theclassification layer.

FIG. 4 illustrates in a block diagram 400, the automated parsingpipeline architecture according to an embodiment. According to anembodiment, the system is configured to receive input image data from aplurality of capturing devices, or input event sources 402. A processor404 including an image processor, a voxel parsing engine and alocalization layer. The image processor is configured to parse imageinto each image frame and preprocess the parsed image. The voxel parsingengine is configured to configured to localize an anatomical structureresiding in the at least single pre-processed field of view by assigningeach p/v a distinct anatomical structure ID. The localization layer isconfigured to select all p/v belonging to the localized anatomicalstructure by finding a minimal bounding rectangle around the p/v and thesurrounding region for cropping as a defined anatomical structure. Thedetection module 406 is configured to detect the condition of thedefined anatomical structure. The detected condition could be sent tothe cloud/remote server, for automation, to EMR and to proxy healthprovisioning 408. In another embodiment, detected condition could besent to controllers 410. The controllers 410 includes reports andupdates, dashboard alerts, export option or store option to save,search, print or email and sign-in/verification unit.

Referring to FIG. 5, an example screenshot 500 of tooth localizationdone by the present system, is illustrated. This figure shows examplesof teeth segmentation at axial slices of 3D tensor.

Problem: Formulating the problem of tooth localization as a 33-classsemantic segmentation. Therefore, each of the 32 teeth and thebackground are interpreted as separate classes.

Model: A V-Net-based fully convolutional network is used. V-Net is a6-level deep, with widths of 32; 64; 128; 256; 512; and1024. The finallayer has an output width of 33, interpreted as a softmax distributionover each voxel, assigning it to either the background or one of 32teeth. Each block contains 3*3*3 convolutions with padding of 1 andstride of 1, followed by ReLU non-linear activations and a dropout with0:1 rate. Instance normalization before each convolution is used. Batchnormalization was not suitable in this case, as long as there is onlyone example in batch (GPU memory limits); therefore, batch statisticsare not determined.

Different architecture modifications were tried during the researchstage. For example, an architecture with 64; 64; 128; 128; 256; 256units per layer leads to the vanishing gradient flow and, thus, notraining. On the other hand, reducing architecture layers to the firstthree (three down and three up) gives a comparable result to theproposed model, though the final loss remains higher.

Loss function: Let R be the ground truth segmentation with voxel valuesri (0 or 1 for each class), and P the predicted probabilistic map foreach class with voxel values pi. As a loss function we use soft negativemulti-class Jaccard similarity, that can be defined as:

${{Jaccard}\mspace{14mu}{Milti}\mspace{14mu}{class}\mspace{14mu}{Loss}} = {1 - {\frac{1}{N}{\sum\limits_{i = 0}^{N}\frac{{{p_{i}r_{i}} +} \in}{{p_{i} + r_{i} - {p_{i}r_{i}} +} \in^{\prime}}}}}$

where N is the number of classes, which in our case is 32, and ϵ is aloss function stability coefficient that helps to avoid a numericalissue of dividing by zero. Then the model is trained to convergenceusing an Adam optimizer with learning rate of 1e-4 and weight decay1e-8. A batch size of 1 is used due to the large memory requirements ofusing volumetric data and models. The training is stopped after 200epochs and the latest checkpoint is used (validation loss does notincrease after reaching the convergence plateau).

Results: The localization model is able to achieve a loss value of 0:28on a test set. The background class loss is 0:0027, which means themodel is a capable 2-way “tooth/not a tooth” segmentor. The localizationintersection over union (IoU) between the tooth's ground truthvolumetric bounding box and the model-predicted bounding box is alsodefined. In the case where a tooth is missing from ground truth and themodel predicted any positive p/v (i.e. the ground truth bounding box isnot defined), localization IoU is set to 0. In the case where a tooth ismissing from ground truth and the model did not predict any positive p/vfor it, localization IoU is set to 1. For a human-interpretable metric,tooth localization accuracy which is a percent of teeth is used thathave a localization IoU greater than 0:3 by definition. The relativelylow threshold value of 0:3 was decided from the manual observation thateven low localization IoU values are enough to approximately localizeteeth for the downstream processing. The localization model achieved avalue of 0:963 IoU metric on the test set, which, on average, equates tothe incorrect localization of 1 of 32 teeth.

Referring to FIGS. 6A-6C, an example screenshot (600A, 600B, 600B) oftooth sub-volume extraction done by the present system, illustrated.

In order to focus the downstream classification model on describing aspecific tooth of interest, the tooth and its surroundings is extractedfrom the original study as a rectangular volumetric region, centered onthe tooth. In order to get the coordinates of the tooth, the upstreamsegmentation mask is used. The predicted volumetric binary mask of eachtooth is preprocessed by applying erosion, dilation, and then selectingthe largest connected component. A minimum bounding rectangle is foundaround the predicted volumetric mask. Then, the bounding box is extendedby 15 mm vertically and 8 mm horizontally (equally in all directions) tocapture the tooth and surrounding region (tooth context) and to correctpossibly weak localizer performance. In other embodiments, the minimumbounding box may be any length in either direction to optimally capturetooth context. Finally, a corresponding sub-volume is extracted from theoriginal clipped image, rescale it to 643 and pass it on to theclassifier. An example of a sub-volume bounding box is presented inFIGS. 6A-6C.

Referring to FIG. 7, a receiver operating characteristic (ROC) curve 700of a predicted tooth condition is illustrated.

Model: The classification model has a DenseNet architecture. The onlydifference between the original and implementation of DenseNet by thepresent invention is a replacement of the 2D convolution layers with 3Dones. 4 dense blocks of 6 layers is used, with a growth rate of 48, anda compression factor of 0:5. After passing the 643 input through 4 denseblocks followed by down-sampling transitions, the resulting feature mapis 548×2×2×2. This feature map is flattened and passed through a finallinear layer that outputs 6 logits—each for a type of abnormality.

Loss function: Since tooth conditions are not mutually exclusive, binarycross entropy is used as a loss. To handle class imbalance, weight eachcondition loss inversely proportional to its frequency (positive rate)in the training set. Suppose that Fi is the frequency of condition i, piis its predicted probability (sigmoid on output of network) and ti isground truth. Then: Li=(1=Fi). ti .log pi+Fi. (1−ti).log(1−pi) is theloss function for condition i. The final example loss is taken as anaverage of the 6 condition losses.

Artificial Filling Impacted crowns canals Filling tooth Implant MissingROC AUC 0.941 0.95 0.892 0.931 0.979 0.946 Condition 0.092 0.129 0.2150.018 0.015 0.142 frequency

Results: The classification model achieved average area under thereceiver operating characteristic curve (ROC AUC) of 0:94 across the 6conditions. Per-condition scores are presented in above table. Receiveroperating characteristic (ROC) curves 700 of the 6 predicted conditionsare illustrated in FIG. 7.

FIG. 8 illustrates a method flow diagram of exemplary steps in theconstruction of an EoI-focused panorama in accordance with an aspect ofthe invention. It is to be understood that references to anatomicalstructures may also assume image or image data corresponding to thestructure. For instance, extracting a teeth arch translates toextracting the portion of the image wherein the teeth arch resides, andnot the literal anatomical structure. FIG. 8 generally illustrates amethod of steps for constructing a 2D panoramic image from a 3D CBCTstudy. More specifically, in one embodiment, disclosed is a method forconstructing a panorama of teeth arch with elements of interestemphasized. In a preferred embodiment, the method comprises the step of:(1) extracting a teeth arch from a volumetric image and unfolding theextracted teeth arch into a panoramic ribbon 802; and step (2) assigningweighted priorities to at least two points in the panoramic ribbon,wherein priorities are weighted higher for points inside or proximal toelements of interest (EoI) and applying a weighted summation in adirection perpendicular to teeth arch resulting in the panorama with EoIemphasized 804.

The construction of the EoI-focused panorama of an oral complex (or morespecifically, a teeth arch) is generally achieved in two steps accordingto one embodiment: The first step concludes in voxel coordinatesoperations, such as extracting teeth arch and unfolding a study imageinto 3D panoramic ribbon (curved sub-volume that passes along the teetharch). Teeth arch is extracted using segmentations of teeth and anatomy,mandible and mandibular canals in particular. Anatomy segmentation mayalso required to maintain stable extraction in case of missing teeth andto ensure that TMJs and canals are visualized in panorama. Then,construct a transformation grid that allows the arch to unfold instraight line, resulting in a panoramic ribbon. In one embodiment, thetransformation grid is a calculation of the coordinates according to thecoordinates of the original volumetric image. Once unfolded, in oneembodiment, the resulting image is virtually tilted in sagittal planefor frontal teeth apexes to take the most perceptible position. Tiltingof the ribbon to maximize perceptibility of a frontal teeth apex is doneby calculating the angles of frontal section tilt for both sections,applying the transformations according to the calculated tilt during theprocess of calculating of the coordinates of a transformation grid, sothe panoramic ribbon is tilted virtually in non-distorting manner.Alternatively, the unfolding of the arch in a straight line forgenerating the ribbon does not require applying a transformation grid.In other embodiments, the unfolding of the arch into the ribbon does notsubsequently tilt to maximize frontal teeth apex perceptibility.

During the second step we assign priorities to each point in panoramicribbon. Priorities are defined as high in points that are inside orclose to regions of interest (such as teeth, bone, and mandibularcanals) and as low in points far from them. Final panoramic image isobtained by weighted summation in the direction perpendicular to teetharch, where weights are priorities. This results in a panorama whereelements of interest are emphasized in non-distorting manner, asillustrated in FIG. 9 (screen-shot of an exemplary constructedEoI-focused panorama in accordance with an aspect of the invention). Ingeneral, two types of panoramas may be constructed using the abovementioned method: (1) a general panorama; and (2) a split panorama. Ageneral panorama includes RoI for all the teeth present, a wholemandible with TMJs, and a lower part of maxilla with sinuses cropped tothe middle. A split panorama is constructed for both jaws separately andis focused only on teeth—as shown in FIG. 9. Alternatively, a method forgenerating a split or general panorama may be done with the followingsteps, beginning with (1) combining teeth and mandible segmentations;(2) building an axial projection of segmentation combination; (3)building a skeleton of the axial projection; (4) dividing the skeletoninto all possible paths; (5) taking a longest path; (6) ensuring thatthis path is long enough to cover TMJs, if they are present; (7)smoothing the path; and then (8) returning it as the teeth arch.

While not illustrated in FIG. 8, one embodiment of the method flow mayentail: As a first step, receiving an i/v.i (image/volumetric image)further parsed into at least a single image frame field of view by thevolumetric image processor. Optionally, the at least single image framefield of view may be pre-processed by controlling image intensity valueby the volumetric image processor. As a next step, an anatomicalstructure residing in the at least single field of view is localized byassigning each p/v (pixel/voxel) a distinct anatomical structure ID bythe voxel parsing engine. Next, all p/v belonging to the localizedanatomical structure is selected by finding a minimal bounding rectanglearound the p/v and the surrounding region for cropping as a definedanatomical structure by the localization layer. Optionally, a visualreport may be reconstructed with defined and localized anatomicalstructure. Also optionally, conditions for each defined anatomicalstructure may be classified within the cropped image by theclassification layer. Next, extracting a teeth arch from the localizedimage generated and unfolding the extracted teeth arch into a panoramicribbon; and finally, assigning weighted priorities to at least twopoints in the panoramic ribbon, wherein priorities are weighted higherfor points inside or proximal to elements of interest (EoI) and applyinga weighted summation in a direction perpendicular to teeth archresulting in the panorama with EoI emphasized.

Now in reference to FIG. 10—depicting a system block diagram of theEoI-focused panorama construction in accordance with an aspect of theinvention. A system for constructing an Elements of Interest(EoI)-focused panorama may comprise: a processor 1008; a non-transitorystorage element coupled to the processor 1008; encoded instructionsstored in the non-transitory storage element, wherein the encodedinstructions when implemented by the processor 1008, configure thesystem to: extract a teeth arch from a volumetric image; unfold theextraction into a panoramic ribbon; assign weighted priorities to eachpoint in the ribbon, wherein priorities are weighted higher for pointsinside or proximal to an EoI; and apply a weighted summation in adirection perpendicular to teeth arch, resulting in the panorama withthe EoI emphasized.

As shown in FIG. 10, the system may comprise: a volumetric imageprocessor 1008 a, a voxel parsing engine 1008 b, a localization layer1008 c, an EoI engine 1008 d, an instance segmentation module 1008 e,wherein the system, and more particularly, the EoI engine 1008 d may beconfigured to: extract a teeth arch from a volumetric image; form astudy image from the extract; unfold the study image into a panoramicribbon; tilt the ribbon for maximal frontal teeth exposure; assignweighted priorities to at least two points in the ribbon, whereinpriorities are weighted higher for points inside or proximal to EoI;apply a weighted summation in a direction perpendicular to teeth archresulting in the panorama with EoI emphasized; and apply the instancesegmentation module 1008 e to accurately provide a segmentation mask andnumbering with EoI emphasized.

In one embodiment, the teeth arch is extracted using segmentations of atleast one of a teeth or anatomy by the EoI engine 1008 d. Alternatively,the teeth arch extraction is done by an algorithm that combines teethand mandible segmentations, extracts the teeth arch landmarks in 2Dplane, fits a pre-defined function in form of a default teeth arch tothe extracted landmarks, and returns a fitted function as the teetharch. In one embodiment, the panoramic ribbon is a curved sub-volumepassing along the teeth arch, created from unfolding the image of theextracted teeth arch. Alternatively, the extension of the teeth archfrom 2D plane to 3D is done by an algorithm that extracts vestibuloralslices from a curved sub-volume of teeth and anatomy segmentationspassing along the 2D teeth arch; construct a vertical curve thatapproximates position and tilt of upper and lower teeth and anatomy oneach axial level at each vestibulooral slice; combine the extractedvertical curves in a way that points of each curve on each axial levelresulting in a 2D teeth arch specific for a given axial level; andreturn a vertical curve combination as a 3D teeth arch.

In an embodiment, once the 3D panoramic ribbon is generated, prioritiesare assigned to a plurality of points arbitrarily chosen on thepanoramic ribbon. The arbitrarily chosen points are evenly spaced alongthe length of the panoramic ribbon. In other embodiments, the points maynot be chosen arbitrarily, but rather, according to a pre-defined rule.The elements of interest may be at least one of a bone, tooth, teeth, ormandibular canals. wherein weights are assigned highest to points insideor most proximal to elements of interest with a pre-defined highestvalue.

While not shown in FIG. 10, in another embodiment, the system may beconfigured to receive input image data from a plurality of capturingdevices, or input event sources. A processor, including or in additionto, an image processor, a voxel parsing engine, a localization layer,and an EoI Engine may be comprised within the system. An image processormay be configured to parse the captured image into each image frame and,optionally, preprocess the parsed image. A voxel parsing engine may beconfigured to localize an anatomical structure residing in the at leastsingle field of view by assigning each p/v a distinct anatomicalstructure ID. The localization layer may be configured to select all p/vbelonging to the localized anatomical structure by finding a minimalbounding rectangle around the p/v and the surrounding region forcropping as a defined anatomical structure. Optionally, a detectionmodule may be configured to detect the condition of the definedanatomical structure. Also, optionally, the detected condition could besent to the cloud/remote server, for automation, to EMR and to proxyhealth provisioning. In another embodiment, detected condition could besent to controllers, for generating reports and updates, dashboardalerts, export option or store option to save, search, print or emailand sign-in/verification unit. Once localized, the EoI engine may beconfigured to: extract a teeth arch from the localized image;optionally, form a study image from the extract; unfold the extractedteeth arch, or optionally, the study image into a panoramic ribbon; tiltthe ribbon for maximal frontal teeth exposure; assign weightedpriorities to at least two points in the ribbon, wherein priorities areweighted higher for points inside or proximal to EoI; apply a weightedsummation in a direction perpendicular to teeth arch resulting in thepanorama with EoI emphasized; and, optionally, apply the instancesegmentation module to accurately provide a segmentation mask andnumbering with EoI emphasized.

In one embodiment, a segmentation module 1008 e (optionally, an instancesegmentation module), operating over 2D panoramic image plane, providesaccurate teeth segmentation masks and numbering to a corresponding 3DCBCT image. The initial step is to segment teeth instances on anautomatically generated panoramic image. Using state-of-the-art 2Dinstance segmentation deep learning models (R-CNN detectors), 1)localize teeth in 2D bounding boxes; 2) assign a number to each detectedtooth in accordance with the dental formula; and 3) provide accurate 2Dmasks to each detected tooth. To train 2D instance segmentation module,utilize the mixture of the annotated OPT and panoramic images generatedfrom CBCT, obtained as an output of our automatic panoramic generator.With the assistance of the 3D panoramic ribbon, retrieve 3D boundingboxes, inferred from the 2D panoramic instances, defining correspondencebetween 2D and 3D bounding boxes coordinates. The 3D bounding boxes (asregions of interest) are further submitted to 3D segmentation module, toobtain accurate 3D teeth masks in the original CBCT fine scale.

In further clarification, using one of the module's output (3D-UNet or3D-R-CNN), use teeth masks to automatically construct panoramic surface,which defines mathematically; independently project the 3D tooth masksof both modules on a panoramic surface which gives 2D tooth masksprojections. In order do this, calculate normal vectors to the panoramicsurface and project every pixel of a mask on this surface. In parallelwith the previous step, apply the third, 2D R-CNN style instancesegmentation model, to the generated panorama image to acquire 2D toothmasks and labels. For every 2D R-CNN mask, pick an instance either from3D-UNet or 3D-R-CNN. This step is accomplished by calculating anIntersection over Union (IoU) for 2D masks and the projected 3D masks,selecting the best 3D projections for every 2D instance on the panoramicimage detected by 2D R-CNN detector. Since the relations between the 3Dprojected masks and the 3D masks themselves are understood, picking a 3Dprojected mask infers picking the corresponding 3D mask.

In one embodiment, the segmentation module 1008e (optionally, semanticsegmentation modules or 3D U-Nets) is configured to specifically segmentlocalized pathologies, like caries and periapical lesions. We use thesesegmentation to 1) estimate volume of the lesions to track it sizedynamic over time, 2) create very specific visualizations (slice thatcuts right at the maximum volume of the lesion), 3) get a “secondopinion” network for diagnostics. These networks could also be trainedin multi-task fashion, where one network learns to segment multipletypes of lesions. A classification module could also be attached to theoutputs of any network layer(s) to produce probabilities for lesion orwhole-image. In case of whole-image classifier, it could be used to addcheaper weakly labeled data (single whole-image label “image containslesions”/“image does not contain lesions”) to costly segmentation labels(assigning lesion/background to each voxel). These networks typicallyoperate on RoI defined by tooth sub-volume, like the Descriptor.However, non-dental diseases such as tumors and cysts in the jaws mayalso be diagnosed. In those cases, different RoI, like RoI of theupper/lower jaw, may be used.

Besides teeth, an anatomy of the skull may be segmented: Mandible andmaxilla mandibular canal Sinuses, for instance. The combination ofanatomy, along with teeth, may be exported as STL models to third partyapplications. The segmentation of mandibular canal to characterize atooth's relation with it, e.g. “tooth is really close to canal”, may beperformed. This is relevant for surgery planning and diagnostics.Furthermore, a mandible and maxilla segmentation may be performed todiagnose gum disease and to select RoI for additional processing (e.g.tooth segmentation). Even furthermore, sinuses may be segmented toselect RoI for sinus diagnosis.

A root-canal system localization module may be used to accuratelysegment all roots, canals inside them and pulp chamber for eachpresented tooth. A 3D U-Net based CNN architecture may be used to solvea multiclass semantic segmentation problem. Precise roots and canalssegmentation affords a diagnostician/practitioner to estimate canalslength, their curvature and visualize the most informative tooth slicesin any point, direction, and with any size and thickness. This moduleallows to see and understand the anatomy of dental roots and canals andforms the basis for planning an endodontic treatment.

Gum disease is a loss of bone around a tooth. Inflammation around teethmay cause bone to recede and expose a tooth's roots. Gum diseasediagnosis is performed by measuring a bone loss from a cemento-enameljuntion (CEJ, line on tooth's crown where enamel ends) to the beginningof bone envelope. Diagnosis is performed by segmenting 1) tooth's body,2) enamel, 3) alveolar bone (tooth's bony envelope), and thenalgorithmically measuring what part of a tooth between apex and CEJ iscovered by the bone.

These are two types of artefacts that may corrupt an image, rendering itunusable for diagnostic purposes. A model developed takes in 2D axialpatches, centered on teeth, and predicts if the given CBCT is affectedby an artefact, and also predicts the intensity of an artefact. If thisprobability is high, re-taking the image is recommended. In extremecases, an intensity score may be assigned corresponding to the severityof the artefact.

A model developed finds locations of cephalometric landmarks on CBCT.Based on this, a calculation of cephalometric measurements may bepreformed, which are relations (distances and angles) between sets oflandmarks. Based on measurements, a screening may be performed, whichmay signal if patient might have an aesthetic/functional orthodonticproblem and would benefit from a consult with an orthodontic.

A report that serves as a guide for implantology planning for a specificarea has also been developed. A report consists of a panorama and agroup of vestibular slices. A panorama may be constructed using virtualrotation that aligns occlusal plane. A panorama serves as a topogramthat shows the locations of every slice. Slices have a tilt that isintended to match the implant placement direction. Some of the slicesare provided with measurements that are present only if there is a placefor implant. The distance can be estimated 1) as a horizontal platesituated in the implant entrance area that is usually tilted in theimplant placement direction, 2) from the center of the plate to theclosest point of either mandibular canal or maxillary sinus, 3) as avertical from the oral end of plate to the farthest point of mandible.

Also developed is an endodontic treatment planning report generated froman uploaded CBCT image and chosen area of interest on the dentalformula. A series of slices are then obtained: axial, cross-sectionalcanal shape, periapical lesions, C-shaped root canal, and rootfurcation. The report consists of several modules: the panoramic imageof upper and/or lower jaw (depends on the region of interest), rootcanal space, root canal system, root canal shape, function, andperiapical lesions. Optionally, a root canal system anatomy may beassessed and an evaluation of possible endodontic pathology. Furtheroptionally, a report may be generated, which can be stored or handedover to the patient.

A report may be generated that provides necessary visual informationabout a specific third molar using the knowledge of tooth location. Areport consists of three slice sections that differ in sliceorientation: vestibular, axial, and mesiodistal slices. Every slicesection has a topogram image that shows slice locations. A mandibularcanal segmentation may be performed to visualize its location ontopograms to notify a surgeon about tooth-canal relation.

As a part of mandible/maxilla, TMJ parts: Condyle and temporal bone(this step is optional, landmarks could be detected) may be segmented.Then, several landmarks on condyle and temporal bone may be detected anddistances between them measured. These measurements and their relations,e.g. asymmetry, may be a basis for TMJ diagnosis and referral foradditional study via MRI. TMJ disorders can cause pain and are typicallycounter-indications for orthodontic treatment.

Similar to pathology localizers, a model has been developed that willsegment pathologies on panoramic radiograph. It operates over the entireimage or inside a tooth RoI (bounding box predicted by OPT localizer).Any type of 2D segmentation network can be used, e.g. UNet. Afterpathology segmentation is obtained, assign pathologies to tooth byselecting those that are inside predicted tooth mask or are immediatelyadjacent to it.

Superimposition of several CBCTs are important for assessing changesbetween two time points. This may be performed in one of the followingways:

-   1) General SI: predict ceph landmarks and orient one image onto    another by minimizing the distance between individual ceph points on    the image.-   2) During superimposition, some landmarks can be ignored if they are    significantly changed, i.e., we minimize distance between points    EXCEPT N points that have maximum distances after algorithm has    converged.-   3) Tooth-related SI: predict some tooth landmarks, like apex/radix    points, furcation points, crown “bumps” and “fissures”. Then do    minimization of distances.-   4) Generic mask-based SI: select some region, e.g., region around    the tooth, and segment anatomy in it. Put a fine grid over this    region. For each grid point, see which anatomical area is detected    there.    Then do SI by minimizing distances between all similar anatomical    regions and maximizing distance between dissimilar ones.

A localizer-descriptor pipeline for segmentation of teeth+detection ofpathologies for intraoral x-rays (bitewing, periapical, occlusal images)has also been developed. The pipeline is similar to the CBCT/OPTdescribed above (localizer/descriptor pipeline). The pipeline may alsobe configured for segmentation of teeth+detection of pathologies forintraoral photography (optical-visible light-based). Developed also is alocalizer-descriptor pipeline for segmentation of teeth+detection ofpathologies for intraoral optical scans (3D surface obtained with highlyprecise laser depth sensor and configured for optical texture-visiblelight-display).

Developed is a module that creates 3D models from tooth and anatomymasks. It uses marching cubes algorithm followed by laplacian smoothingto output a 3D mesh, which is later saved to STL. Certain anatomicalobjects can be grouped together to provide a surface containing selectedobjects (e.g. jaw+all teeth except one that is marked for extraction,for the purposes of a separate 3D-printing a surgical template forprecise drilling of the implant hole).

Finally, a module that co-registers CBCT and IOS taken at the same timehas been developed: Input is CBCT and IOS in separate coordinatesystems. The output is IOS translated to coordinate system of CBCT. IOShave higher resolution, while CBCT displays the insides of thetooth/jaw. This is achieved by detecting the same dental landmarks(distinct points on teeth) on CBCT and IOS; at which point, minimize thedistance between same points on CBCT and on IOS, which will give acoordinate transform to apply to IOS.

Advantageously, the present invention provides an end-to-end pipelinefor detecting state or condition of the teeth in dental 3D CBCT scans.The condition of the teeth is detected by localizing each present toothinside an image volume and predicting condition of the tooth from thevolumetric image of a tooth and its surroundings. Further, theperformance of the localization model allows to build a high-quality 2Dpanoramic reconstruction—with EoI focused—which provides a familiar andconvenient way for a dentist to inspect a 3D CBCT image. The performanceof the pipeline—with image processor, parsing engine, localizationlayer, EoI engine, and segmentation modules—is improved by adding i/v.idata augmentations during training; reformulating the localization taskas instance segmentation instead of semantic segmentation; reformulatingthe localization task as object detection, and use of different classimbalance handling approaches for the classification model.Alternatively, the jaw region of interest is localized and extracted asa first step in the pipeline. The jaw region typically takes around 30%of the image/image volume and has adequate visual distinction.Extracting it with a shallow/small model would allow for largerdownstream models. Further, the diagnostic coverage of the presentinvention extends from basic tooth conditions to other diagnosticallyrelevant conditions and pathologies.

FIG. 11 illustrates in a block diagram, an automated parsing pipelinesystem for landmark localization and condition classification 1100 inaccordance with an aspect of the invention. In an embodiment, the systemcomprises an input system 1101, a memory system or unit 1102, and aprocessor system 1103. As shown, the processor system 1103 comprises avolumetric image processor 1103 a, a voxel parsing engine 1104 incommunication with the volumetric image processor 1103 a, a localizationlayer 1105 in communication with the voxel parsing engine 1104 and aclassification layer 1106 in communication with the localization layer1105. The processor 1103 is configured to receive at least one i/v.i viaan input system 1101. At least one received i/v.i, which may becomprised of a 2-D or 3-D image. The pixel array is optionallypre-processed to convert into an array of Hounsfield Unit (HU) radiointensity measurements. Then, the processor 1103 is configured to parsethe received i/v.i into at least a single image frame by the saidvolumetric image processor 1103 a.

The landmark structures residing in the at least single field of view islocalized by assigning each p/v a distinct landmark structure by thevoxel parsing engine 1104. The processor 1103 is configured to selectall p/v belonging to the localized landmark structure by finding aminimal bounding rectangle around the p/v and the surrounding region forcropping as a defined landmark structure by the localization layer 1105.Then, the conditions for each defined landmark structure within thecropped image is classified by a detection module or classificationlayer 1106. In one embodiment, the classification is achieved using anyone of a FCN/CNN, such as a DenseNet 3-D convolutional neural network—oralternatively, by processing anthropomorphic features within the definedlandmark or between landmarks. In some embodiments, the definedlandmarks targeted for localization and classification may be landmarkscomprising features associated with a pathological significance. Inother embodiments, the targeted landmarks for localization andclassification are not associated with a pathological significance, butrather are standard dental landmarks:

Exemplary Dental Anatomical Landmarks:

-   Fauces—Passageway from oral cavity to pharynx.-   Frenum—Raised folds of tissue that extend from the alveolar and the    buccal and labial mucosa.-   Gingiva—Mucosal tissue surrounding portions of the maxillary and    mandibular teeth and bone.-   Hard palate—Anterior portion of the palate which is formed by the    processes of the maxilla.-   Incisive papilla—A tissue projection that covers the incisive    foramen on the anterior of the hard palate, just behind the    maxillary central incisors.-   Maxillary tuberosity—A bulge of bone posterior to the most posterior    maxillary molar.-   Maxillary/Mandibular tori—Normal bony enlargements that can occur    either on the maxilla or mandible.-   Mucosa—Mucous membrane lines the oral cavity. It can be highly    keratinized (such as what covers the hard palate), or lightly    keratinized (such as what covers the floor of the mouth and the    alveolar processes) or thinly keratinized (such as what covers the    cheeks and inner surfaces of the lips).-   Palatine rugae—Firm ridges of tissues on the hard palate.-   Parotid papilla—Slight fold of tissue that covers the opening to the    parotid gland on the buccal mucosa adjacent to maxillary first    molars.-   Pillars of Fauces—Two arches of muscle tissue that defines the    fauces.-   Soft palate—Posterior portion of the palate. This is non-bony and is    comprised of soft tissue.-   Sublingual folds—Small folds of tissue in the floor of the mouth    that cover the openings to the smaller ducts of the sublingual    salivary gland.-   Submandibular gland—Located near the inferior border of the mandible    in the submandibular fossa.-   Tonsils—Lymphoid tissue located in the oral pharynx.-   Uvula—A non-bony, muscular projection that hangs from the midline at    the posterior of the soft palate.-   Vestibule—Space between the maxillary or mandibular teeth, gingiva,    cheeks and lips.-   Wharton's duct—Salivary duct opening on either side of the lingual    frenum on the ventral surface of the tongue.

In one embodiment, the pipeline 110 is configured to classify each p/vas one of a feature/landmark and resulting segmentation assigns each p/vto one of a landmark class. In one embodiment, the system utilizesfully-convolutional network to perform at least one of the localization,anthropomorphic measurements, or condition classification. In oneembodiment, the system comprises FCN/CNN (such as VNet) predictingmultiple “energy levels”, which are later used to find boundaries. Inanother embodiment, a recurrent neural network could be used for step bystep prediction of landmark. In yet another embodiment, Mask-RCNNgeneralized to 3D could be used by the system. In yet anotherembodiment, the system could take multiple crops from 3D image inoriginal resolution, perform instance segmentation, and then join cropsto form mask for all original image. In another embodiment, the systemcould apply either segmentation or object detection in 2D, to segmentaxial slices. This would allow to process images in original resolution(albeit in 2D instead of 3D) and then infer 3D shape from 2Dsegmentation.

In one embodiment, the system could be implemented utilizing descriptorlearning in the multitask learning framework i.e., a single networklearning to output predictions for multiple dental conditions. Thiscould be achieved by balancing loss between tasks to make sure everyclass of every task have approximately same impact on the learning. Theloss is balanced by maintaining a running average gradient that networkreceives from every class*task and normalizing it. Alternatively,descriptor learning could be achieved by teaching network on batchesconsisting data about a single condition (task) and sample examples intothese batches in such a way that all classes will have same number ofexamples in batch (which is generally not possible in multitask setup).Further, standard data augmentation could be applied to 3D tooth imagesto perform scale, crop, rotation, vertical flips. Then, combining allaugmentations and final image resize to target dimensions in a singleaffine transform and apply all at once.

In some embodiments, the system could use coarse segmentation mask fromlocalizer as an input instead of tooth image. In some embodiments, thedescriptor could be trained to output fine segmentation mask from someof the intermediate layers. In some embodiments, the descriptor could betrained to predict tooth number. As an alternative to multitask learningapproach, “one network per condition” could be employed, i.e. models fordifferent conditions are completely separate models that share noparameters. Another alternative is to have a small shared base networkand use separate subnetworks connected to this base network, responsiblefor specific conditions/diagnoses.

FIG. 12 depicts an interaction flow diagram of the automated parsingpipeline for landmark localization and condition classification inaccordance with an aspect of the invention. In one embodiment (shown),the system is configured to receive input image data from a plurality ofcapturing devices, or input event sources 1202. A processor 1204including at least an image processor, a voxel parsing engine 1204 and alocalization layer 1204 is configured to parse image/s into each imageframe and localize a landmark structure (of anatomical or pathologicalsignificance) residing in the at least single (optionally,pre-processed) field of view/parsed frame by assigning each p/v adistinct landmark structure ID. The localization layer 1204 isconfigured to select all p/v belonging to the localized landmarkstructure by finding a minimal bounding rectangle around the p/v and thesurrounding region for cropping as a defined landmark structure. Thedetection module or classification layer 1206 is configured to detectthe condition of the defined landmark structure by making variousmeasurements of at least one of a detected feature within a definedlandmark, between detected features within a defined landmark, orbetween detected features between defined landmarks. In anotherembodiment, the condition is detected by the classification layer 1206performing any one of a FCN/CNN, such as a DenseNet 3-D convolutionalneural network.

In one embodiment, the detected condition could be sent to thecloud/remote server/third-party, for automation, to EMR and to proxyhealth provisioning 1208. In another embodiment, the detected conditioncould be sent to a server for generation of reports and updates,dashboard alerts, export option or store option to save, search, printor email and sign-in/verification unit without a need for an API 1210.

FIG. 13 illustrates a method flow depicting the steps of the automatedparsing pipeline for landmark localization and condition classificationin accordance with an aspect of the invention. At step 1312, an inputimage data is received. Next, at step 1314, the received i/v.i is parsedinto at least a single image frame field of view. Optionally, the parsedi/v.i is pre-processed either by controlling image intensity value;rescaling using linear interpolation; or converting the 3-D pixel arrayinto an array of Hounsfield Unit (HU). At step 1316, a landmarkstructure or landmark features inside the parsed i/v.i is localized andidentified by landmark number (standardized or arbitrary). Furthermore,a landmark structure residing in the at least single field of view islocalized by assigning each p/v a distinct landmark structure ID by thevoxel parsing engine. At step 1318, the identified landmark/features andsurrounding context within the localized i/v.i are extracted; all p/vbelonging to the localized landmark structure is selected by finding aminimal bounding rectangle around the p/v and the surrounding region forcropping as a defined landmark structure by the localization layer. Atstep 1320, conditions for each defined landmark structure is classifiedwithin the cropped image by the classification layer performing deeplearning techniques or anthropomorphic measurements. At step 1322, avisual report/dashboard analytics may be created with localized anddefined landmark structure. In some embodiments, the visual reportsinclude, but not limited to, an endodontic report (with focus on tooth'sroot/canal system and its treatment state), an implantation report (withfocus on the area where the tooth is missing), and a dystopic toothreport for tooth extraction (with focus on the area of dystopic/impactedteeth).

In one embodiment, the classifier, or classifying module, orclassification layer, or even detection module may performanthropomorphic measurements by any of: measuring a distance between atleast two points; measuring an angle between three points inthree-dimensional space; measuring a distance between a plane defined bythree points; any other kind of measurement of relation between any setsof points. Furthermore, anthropomorphic classifying may further compriseat least one of comparing previously measured distance or angles to areference range (threshold parameters); comparing two distances orangles, or comparing a difference of two distances or angles to thereference range (threshold parameters).

For instance, in the event of localizing the landmark or landmarkfeatures of a cement-enamel junction (CEJ) and bone attachment points(BAP) of a tooth, the distance measured between the two features aboveor below a threshold may determine periodontal bone loss. The CEJ pointand BAP of a tooth alongside 2, 4 or more sides of the tooth (i.e.buccal, lingual, mesial, distal, buccal-distal, buccal-mesial, or anykind of intermediate points) may be measured in terms of distancebetween points to define a BAP-CEJ value, which may be indicative of thefollowing diagnosis: “No periodontal bone loss” if <1mm; “Mildperiodontal bone loss” if 1 to 3 mm; “Moderate periodontal bone loss” if3 to 5 mm; “Severe periodontal bone loss” if more than 5 mm. Actualranges are extracted from literature and are subject toreview/change/tuning. Furthermore, another landmark/landmark featuresubject to localization/diagnostic may be cephalometric features on amaxillofacial region, whereby an angle measured between cephalometricfeatures above or below a threshold indicate an orthodontic condition.The anthropomorphic diagnostic may be in lieu of, or in conjunctionwith, deep neural network applications to further validate adiagnosis—whether periodontal or orthodontic.

The figures illustrate the architecture, functionality, and operation ofpossible implementations of systems, methods and computer programproducts according to various embodiments of the present invention. Itshould also be noted that, in some alternative implementations, thefunctions noted/illustrated may occur out of the order noted. Forexample, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved.

Since various possible embodiments might be made of the above invention,and since various changes might be made in the embodiments above setforth, it is to be understood that all matter herein described or shownin the accompanying drawings is to be interpreted as illustrative andnot to be considered in a limiting sense. Thus, it will be understood bythose skilled in the art that although the preferred and alternateembodiments have been shown and described in accordance with the PatentStatutes, the invention is not limited thereto or thereby.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. Additionally, it is to be understoodthat references to anatomical structures may also assume image or imagedata corresponding to the structure. For instance, extracting a teetharch translates to extracting the portion of the image wherein the teetharch resides, and not the literal anatomical structure.

Some portions of embodiments disclosed are implemented as a programproduct for use with an embedded processor. The program(s) of theprogram product defines functions of the embodiments (including themethods described herein) and can be contained on a variety ofsignal-bearing media. Illustrative signal-bearing media include, but arenot limited to: (i) information permanently stored on non-writablestorage media (e.g., read-only memory devices within a computer such asCD-ROM disks readable by a CD-ROM drive); (ii) alterable informationstored on writable storage media (e.g., floppy disks within a diskettedrive or hard-disk drive, solid state disk drive, etc.); and (iii)information conveyed to a computer by a communications medium, such asthrough a computer or telephone network, including wirelesscommunications. The latter embodiment specifically includes informationdownloaded from the Internet and other networks. Such signal-bearingmedia, when carrying computer-readable instructions that direct thefunctions of the present invention, represent embodiments of the presentinvention.

In general, the routines executed to implement the embodiments of theinvention, may be part of an operating system or a specific application,component, program, module, object, or sequence of instructions. Thecomputer program of the present invention typically is comprised of amultitude of instructions that will be translated by the native computerinto a machine-accessible format and hence executable instructions.Also, programs are comprised of variables and data structures thateither reside locally to the program or are found in memory or onstorage devices. In addition, various programs described may beidentified based upon the application for which they are implemented ina specific embodiment of the invention. However, it should beappreciated that any particular program nomenclature that follows isused merely for convenience, and thus the invention should not belimited to use solely in any specific application identified and/orimplied by such nomenclature.

The present invention and some of its advantages have been described indetail for some embodiments. It should also be understood that variouschanges, substitutions and alterations can be made herein withoutdeparting from the spirit and scope of the invention as defined by theappended claims. An embodiment of the invention may achieve multipleobjectives, but not every embodiment falling within the scope of theattached claims will achieve every objective. Moreover, the scope of thepresent application is not intended to be limited to the particularembodiments of the process, machine, manufacture, and composition ofmatter, means, methods and steps described in the specification. Aperson having ordinary skill in the art will readily appreciate from thedisclosure of the present invention that processes, machines,manufacture, compositions of matter, means, methods, or steps, presentlyexisting or later to be developed are equivalent to, and fall within thescope of, what is claimed. Accordingly, the appended claims are intendedto include within their scope such processes, machines, manufacture,compositions of matter, means, methods, or steps.

We claim:
 1. A method for classifying a tooth condition, said methodcomprising the steps of: parsing a received volumetric image into atleast a single image frame; localizing a present landmark in a patient'soral or maxillofacial region, said landmark comprising of a feature orfeatures with anatomical or pathological significance; and classifying adental or maxillofacial condition based on measuring any kind ofrelation between two or more features within and/or between landmarks.2. The method of claim 1, wherein localizing the anatomical landmark isachieved by applying a deep neural network to obtain any kind ofprobability distribution of location of any one of the anatomicallandmark.
 3. The method of claim 1, wherein classifying is achieved byany of: measuring a distance between at least two points; measuring anangle between three points in three-dimensional space; measuring adistance between a plane defined by three points; any other kind ofmeasurement of relation between any sets of points.
 4. The method ofclaim 1, wherein classifying further comprises at least one of comparingpreviously measured distance or angles to a reference range; comparingtwo distances or angles; or comparing a difference of two distances orangles to the reference range.
 5. The method of claim 1, wherein thelandmark features localized are a cement-enamel junction (CEJ) point andbone attachment points (BAP) of a tooth and a distance measured betweenthe two features above or below a threshold determine periodontal boneloss.
 6. The method of claim 1, wherein the landmark features localizedare cephalometric features on a maxillofacial region and an anglemeasured between cephalometric features above or below a thresholddetermine an orthodontic condition.
 7. The method of claim 1, whereinthe at least one received volumetric image comprises a 3-D pixel array.8. The method of claim 1, further comprising converting the 3-D pixelarray into an array of Hounsfield Unit (HU) radio intensity measurementsfor pre-processing the received image.
 9. The method of claim 8, furthercomprising rescaling using linear interpolation for pre-processing thereceived image.
 10. The method of claim 8, further comprisingnormalization schemes to account for variations in intensity forpre-processing the received image.
 11. The method of claim 1, whereinthe landmark localization is achieved using a V-Net-based fullyconvolutional neural network.
 12. The method of claim 1, furthercomprises a step of: achieving extraction for classification of acondition by finding a minimum bounding rectangle around the localizedlandmark.
 13. A system for classifying a tooth condition, said methodcomprising the steps of: a processor; a non-transitory storage elementcoupled to the processor; encoded instructions stored in thenon-transitory storage element, wherein the encoded instructions whenimplemented by the processor, configure the automated parsing pipelinesystem to: parse a received volumetric image into at least a singleimage frame; localize a present anatomical landmark in a patient's oralor maxillofacial region, said landmark comprising of a feature orfeatures with anatomical or pathological significance; and classify adental or maxillofacial condition based on measuring any kind ofrelation between two or more features within and/or between landmarks.14. The system of claim 13, wherein localizing the anatomical landmarkis achieved by applying a deep neural network to obtain any kind ofprobability distribution of location of any one of the anatomicallandmark.
 15. The system of claim 13, wherein classifying is achieved byany of: measuring a distance between at least two points; measuring anangle between three points in three-dimensional space; measuring adistance between a plane defined by three points; any other kind ofmeasurement of relation between any sets of points.
 16. The system ofclaim 13, wherein classifying further comprises at least one ofcomparing previously measured distance or angles to a reference range;comparing two distances or angles; or comparing a difference of twodistances or angles to the reference range.
 17. The system of claim 13,wherein the landmark features localized are a cement-enamel junction(CEJ) point and bone attachment points (BAP) of a tooth and a distancemeasured between the two features above or below a threshold determineperiodontal bone loss.
 18. The system of claim 13, wherein the landmarkfeatures localized are cephalometric features on a maxillofacial regionand an angle measured between cephalometric features above or below athreshold determine an orthodontic condition.
 19. The system of claim13, wherein the at least one received volumetric image comprises a 3-Dpixel array.
 20. The system of claim 13, further comprising convertingthe 3-D pixel array into an array of Hounsfield Unit (HU) radiointensity measurements for pre-processing the received image.
 21. Thesystem of claim 13, further comprising rescaling using linearinterpolation for pre-processing the received image.
 22. The system ofclaim 13, further comprising normalization schemes to account forvariations in intensity for pre-processing the received image.
 23. Thesystem of claim 13, wherein the landmark localization is achieved usinga V-Net-based fully convolutional neural network.
 24. The system ofclaim 13, further comprising finding a minimum bounding rectangle aroundthe localized landmark for achieving extraction for classification of acondition.
 25. A system for classifying a tooth condition, said systemcomprising: a localization layer; a classification layer; a processor; anon-transitory storage element coupled to the processor; encodedinstructions stored in the non-transitory storage element, wherein theencoded instructions when implemented by the processor, configure theautomated parsing pipeline system to: localize a present anatomicallandmark in a patient's oral or maxillofacial region depicted in aparsed image frame by the localization layer, said landmark comprisingof a feature or features with anatomical or pathological significance;and classify a dental or maxillofacial condition based on measuring anykind of relation between two or more features within and/or betweenlandmarks by the classification layer.